import warnings
import datetime
import numpy as np
import pandas as pd
from evolutionary_computation.operators import *
import alphalens_rev as alphalens
warnings.filterwarnings('ignore')
df_pivot = pd.read_pickle(
"./evolutionary_computation/Data/df_pivot/df_pivot_5m.pkl")
require_cols = ['Open', 'High', 'Low', 'Close', 'Volume']
df_pivot = df_pivot.pivot(index="datetime", columns="asset")
# div(ts_min(df_pivot['Open'], 15), df_pivot['Close'])
factors = div(ts_mean(ts_std(df_pivot['Open'], 5), 15), df_pivot['Open'])
factors.index = pd.to_datetime(factors.index)
factors = factors[(factors.index.hour < 12)]
factors = factors[factors.index > "2018-12-14"]
factors.dropna(axis='columns', inplace=True)
pricing = df_pivot['Close']
pricing.index = pd.to_datetime(pricing.index)
pricing = pricing[pricing.index > "2018-12-14"]
def _validate_frequency(cls, index, freq, **kwargs):
return None
pd.core.arrays.datetimelike.DatetimeLikeArrayMixin._validate_frequency = _validate_frequency
non_predictive_factor_data = alphalens.utils.get_clean_factor_and_forward_returns(factors.stack(),
pricing[factors.columns],
_type=-1,
periods=(
6, 12),
quantiles=5)
Dropped 0.0% entries from factor data: 0.0% in forward returns computation and 0.0% in binning phase (set max_loss=0 to see potentially suppressed Exceptions). max_loss is 35.0%, not exceeded: OK!
np.isinf(non_predictive_factor_data).values.sum()
8
non_predictive_factor_data = non_predictive_factor_data.replace(
[np.inf, -np.inf], np.nan)
non_predictive_factor_data = non_predictive_factor_data.fillna(method='ffill')
np.isinf(non_predictive_factor_data).values.sum()
0
alphalens.tears.create_full_tear_sheet(non_predictive_factor_data)
Quantiles Statistics
| min | max | mean | std | count | count % | |
|---|---|---|---|---|---|---|
| factor_quantile | ||||||
| 1 | 0.000000 | 0.013198 | 0.001033 | 0.000719 | 2089702 | 20.173478 |
| 2 | 0.000516 | 0.014903 | 0.001733 | 0.000956 | 2067239 | 19.956626 |
| 3 | 0.000802 | 0.016231 | 0.002328 | 0.001195 | 2067240 | 19.956635 |
| 4 | 0.001125 | 0.021325 | 0.003090 | 0.001490 | 2067239 | 19.956626 |
| 5 | 0.001521 | 1.073325 | 0.005206 | 0.003970 | 2067240 | 19.956635 |
Returns Analysis
| 30m | 1h | |
|---|---|---|
| Ann. alpha | 2748154.259 | 1263.712 |
| beta | 0.404 | 0.380 |
| Mean Period Wise Return Top Quantile (bps) | 1.238 | 1.318 |
| Mean Period Wise Return Bottom Quantile (bps) | -1.098 | -1.154 |
| Mean Period Wise Spread (bps) | 2.336 | 2.467 |
<Figure size 432x288 with 0 Axes>
Information Analysis
| 30m | 1h | |
|---|---|---|
| IC Mean | 0.043 | 0.059 |
| IC Std. | 0.178 | 0.179 |
| Risk-Adjusted IC | 0.243 | 0.330 |
| t-stat(IC) | 36.435 | 49.502 |
| p-value(IC) | 0.000 | 0.000 |
| IC Skew | 0.042 | -0.009 |
| IC Kurtosis | 0.590 | 0.826 |